Is Service Provision Always Equitable? Analyzing Access to Dental Services and Oral Health of Older Adults in Ontario
Bibliographic record
Abstract
The provision of dental services within the Canadian and Ontario healthcare systems is unique, as the majority of dental services are privately delivered. Many vulnerable populations, including older adults, have difficulty accessing dental services because of their private nature, leading to negative oral health outcomes and barriers in accessing dental services. The purpose of this thesis was to explore the oral health outcomes and barriers in accessing dental services among older adults (65+) in Ontario. Following the distribution of an online questionnaire, IBM SPSS 26.0 was used to perform crosstabulations, chi-square tests, and multivariate logistic regression models. The results of the quantitative analysis unveiled relationships between oral health outcomes and barriers in access, with numerous social determinants of health. The oral health outcomes reported were incidences of poor self-rated oral health, tooth removal by a dentist due to decay or gum disease, a lack of having one or more teeth, the use of dentures, having toothaches in the past month, and pain in and around the jaw joints. Respondents who suffered from poor overall health outcomes, or issues pertaining to income and access to insurance were more likely to experience negative oral health outcomes. The barriers in accessing dental services included location within a respondent’s community, proximity to the dentist, the respondent’s relationship with the dentist and staff, the affordability of dental expenses, and the impact of cost on access to dental services. Location and one’s relationship with space and place, and the presence of social support, income, and access to insurance were influential to the types of impacts these barriers created. This research indicates that there is a need for the provincial and federal governments to acknowledge the gaps present surrounding the provision of dental services in the healthcare system. There must be reflection on the usefulness of the current system, as well as change to mitigate the barriers currently in place. Future research can facilitate this change by continuing to analyze the geographic barriers on a narrower and more localized scope, and researching barriers involving other vulnerable populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".